Associate Professor (Specially Appointed), Division of Materials and Manufacturing Science, Osaka University, Japan

Speech Title: Temper Effect Prediction of Laser Temper Bead Welding Repair Technique Using Neural Network
Abstract: Temper bead welding is one of effective repair welding methods instead of post weld heat treatment. With the development and popularization of the laser welding, the advanced laser temper bead welding technique has been developed recently, especially for repairing underwater conditions. However, it is always laborious works to determine the optimum welding condition for laser temper bead welding. Hardness is one of the key criteria to evaluate the tempering effect in temper bead welding. Therefore, in the present study, the hardness prediction system for laser temper bead welding has been constructed using a neural network. Thus, the appropriate welding conditions can be selected before the actual repair welding. On the basis of experimentally obtained hardness database, the neural network-based hardness prediction system for laser temper bead welding has been constructed. With it, the hardness distribution in HAZ of laser temper bead welding was calculated based on the thermal cycles numerically obtained by FEM. The predicted hardness was in good accordance with the experimental results. It follows that the new prediction system is effective for estimating the tempering effect during laser temper bead welding and hence enables us to assess the effectiveness of the laser temper bead welding before the actual repair welding.